Method and apparatus for assigning a confidence level to a term within a user knowledge profile

ABSTRACT

A method of assigning a confidence level to a term within an electronic document, such as an e-mail, includes the step of firstly determining a quantitative indicator in the exemplary form of an occurrence value, based on the number of occurrences of a particular term within an electronic document, and associating the occurrence term within the relevant term. Thereafter, a qualitative indicator, based on a quality of the term, is determined. For example, the qualitative indicator may be determined utilizing the parts of speech of words comprising the term. A confidence level value, which may be utilized to indicate a relative importance of the term in describing a user knowledge base, is then generated utilizing the quantitative and qualitative indicators.

FIELD OF THE INVENTION

The present invention relates generally to the field of knowledgemanagement and, more specifically, to a method and apparatus forassigning a confidence level to a term within a user knowledge profile.

BACKGROUND OF THE INVENTION

The new field of “knowledge management” (KM) is receiving increasingrecognition as the gains to be realized from the systematic effort tostore and export vast knowledge resources held by employees of anorganization are being recognized. The sharing of knowledge broadlywithin an organization offers numerous potential benefits to anorganization through the awareness and reuse of existing knowledge, andthe avoidance of duplicate efforts.

In order to maximize the exploitation of knowledge resources within anorganization, a knowledge management system may be presented with twoprimary challenges, namely (1) the identification of knowledge resourceswithin the organization and (2) the distribution and accessing ofinformation regarding such knowledge resources within the organization.

The identification, capture, organization and storage of knowledgeresources is a particularly taxing problem. Prior art knowledge systemshave typically implemented knowledge repositories that require usersmanually to input information frequently into pre-defined fields, and inthis way manually and in a prompted manner to reveal their personalknowledge base. However, this approach suffers from a number ofdrawbacks in that the manual entering of such information is timeconsuming and often incomplete, and therefore places a burden on userswho then experience the inconvenience and cost of a corporate knowledgemanagement initiative long before any direct benefit is experienced.Furthermore, users may not be motivated to describe their own knowledgeand to contribute documents on an ongoing basis that would subsequentlybe re-used by others without their awareness or consent. The manualinput of such information places a burden on users who then experiencethe inconvenience and cost of a corporate knowledge managementinitiative long before any direct benefit is experienced.

It has been the experience of many corporations that knowledgemanagement systems, after some initial success, may fail because eithercompliance (i.e., the thoroughness and continuity with which each usercontributes knowledge) or participation (i.e., the percentage of usersactively contributing to the knowledge management system) falls toinadequate levels. Without high compliance and participation, it becomesa practical impossibility to maintain a sufficiently current andcomplete inventory of the knowledge of all users. Under these theknowledge management effort may never offer an attractive relationshipof benefits to costs for the organization as a whole, reach a criticalmass, and the original benefit of knowledge management falls apart or ismarginalized to a small group.

In order to address the problems associated with the manual input ofknowledge information, more sophisticated prior art knowledge managementinitiatives may presume the existence of a centralized staff to workwith users to capture knowledge bases. This may however increase theongoing cost of knowledge management and requires a larger up-frontinvestment before any visible payoff, thus deterring the initial fundingof many an otherwise promising knowledge management initiatives. Even ifan initial decision is made to proceed with such a sophisticatedknowledge management initiative, the cash expenses associated with alarge centralized knowledge capture staff may be liable to come underattack, given the difficulty of quantifying knowledge managementbenefits in dollar terms.

As alluded to above, even once a satisfactory knowledge managementinformation base has been established, the practical utilization thereofto achieve maximum potential benefit may be challenging. Specifically,ensuring that the captured information is readily organized, available,and accessible as appropriate throughout the organization may beproblematic.

SUMMARY OF THE INVENTION

According to a first aspect of the invention, there is provided a methodof assigning a confidence level to a term within an electronic document.A first quantitative indicator, based on a number of occurrences of theterm within the electronic document, is determined. A characteristicindicator, based on a characteristic of the term, is determined. Asecond indicator is assigned to the term, the second quantitativeindicator being derived from the first quantitative indicator and thecharacteristic indicator.

According to a second aspect of the invention, there is providedapparatus for assigning a confidence level to a term within anelectronic document. A term extractor extracts of the term from theelectronic document. Confidence logic determines a first indicator basedon a number of occurrences of the term within the electronic document.The confidence logic also determines a characteristic indicator based ona characteristic of the term, and assigns a second quantitativeindicator, derived from the first quantitative indicator and thecharacteristic indicator, to the term.

Other features of the present invention will be apparent from theaccompanying drawings and from the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1 is a block diagram illustrating a knowledge management system,according to an exemplary embodiment of the present invention.

FIG. 2 is a block diagram illustrating a knowledge site managementserver, according to an exemplary embodiment of the present invention.

FIG. 3 is a block diagram illustrating a knowledge access server,according to an exemplary embodiment of the present invention.

FIG. 4 is a block diagram illustrating a knowledge converter, accordingto an exemplary embodiment of the present invention.

FIG. 5 is a block diagram illustrating a client software program, and ane-mail message generated thereby, according to an exemplary embodimentof the present invention.

FIG. 6 is a block diagram illustrating the structure of a knowledgerepository, according to an exemplary embodiment of the presentinvention, as constructed from the data contained in a repositorydatabase and a user database.

FIG. 7 is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of constructing a user knowledgeprofile.

FIG. 8 is a flowchart illustrating a high-level method, according to anexemplary embodiment of the present invention, by which terms may beextracted from an electronic document and by which confidence levelvalues may be assigned to such terms.

FIG. 9A is a flowchart illustrating a method, according to exemplaryembodiment of the present invention, of determining a confidence levelfor a term extracted from an electronic document.

FIG. 9B is a flowchart illustrating a method, according to exemplaryembodiment of the present invention, by which a document weight valuemay be assigned to a document based on addressee information associatedwith the document.

FIG. 10 illustrates a term-document binding table, according to anexemplary embodiment of the present invention.

FIG. 11 illustrates a weight table, according to an exemplary embodimentof the present invention.

FIG. 12 illustrates an occurrence factor table, according to anexemplary embodiment of the present invention.

FIG. 13 illustrates a confidence level table, including initialconfidence level values, according to an exemplary embodiment of thepresent invention.

FIG. 14 illustrates a modified confidence level table, includingmodified confidence level values, according to an exemplary embodimentof the present invention.

FIG. 15A is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of constructing a user knowledgeprofile that includes first and second portions.

FIG. 15B is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of storing a term in either a firstor a second portion of a user knowledge profile.

FIG. 16A illustrates a user-term table, constructed according to theexemplary method illustrated in FIG. 15A.

FIG. 16B illustrates a user-term table, constructed according to theexemplary method illustrated in FIG. 15A.

FIG. 17A is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of facilitating access to a userknowledge profile.

FIG. 17B is a flowchart illustrating an alternative method, according toexemplary embodiment of the present invention, of facilitating access toa user knowledge profile.

FIG. 17C is a flowchart illustrating a method, according to exemplaryembodiment of the present invention, of performing a public profileprocess.

FIG. 17D is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of performing a private profileprocess.

FIG. 17E is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of performing a profilemodification process.

FIG. 18A is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of addressing an electronicdocument for transmission over a computer network.

FIG. 18B is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of executing an “explain” functionthat provides the reasons for the proposal of an e-mail recipient.

FIG. 18C is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of executing a “more” function thatproposes further potential recipients for an e-mail message.

FIG. 18D illustrates a user dialog, according to an exemplary embodimentof the present invention, through which a list of potential recipientsis displayed to an addressor of an e-mail message.

FIG. 19 is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of managing user authorization topublish, or permit access to, a user knowledge profile.

FIG. 20 is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of assigning a confidence value,either in the form of a confidence level value or a confidence memoryvalue, to a term.

FIG. 21 is a flowchart illustrating a method, according to an exemplaryembodiment of the present invention, of determining or identifying aconfidence value, either in the form of a confidence level value or aconfidence memory value, for a term.

FIG. 22 illustrates a user-term table, according to an exemplaryembodiment of the present invention, that is shown to include aconfidence level value column, a confidence memory value column and atime stamp column.

FIG. 23 is a block diagram illustrating a machine, according to oneexemplary embodiment, within which software in the form of a series ofmachine-readable instructions, for performing any one of the methodsdiscussed above, may be executed.

DETAILED DESCRIPTION

A method and apparatus for assigning a confidence level to a term withina user knowledge profile are described. In the following description,for purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Itwill be evident, however, to one skilled in the art that the presentinvention may be practiced without these specific details.

OVERVIEW

With a view to addressing the above described difficulties associatedwith manual knowledge capture either by a profile owner or by adedicated staff, there is provided a method and apparatus for capturingknowledge automatically, without excessive invasion or disruption ofnormal work patterns of participating users. Further, the presentspecification teaches a method and apparatus whereby a database ofcaptured knowledge information is maintained continuously andautomatically, without requiring that captured knowledge informationnecessarily be visible or accessible to others. The presentspecification also teaches facilitating the user input and modificationof a knowledge profile associated with the user in a knowledge databaseat the user's discretion.

The present specification teaches a method and apparatus forintercepting electronic documents, such as for example e-mail messages,originated by a user, and extracting terms therefrom that arepotentially indicative of a knowledge base of the originating user. Theextracted knowledge terms may then be utilized to construct a userknowledge profile. The grammatical structure, length, frequency anddensity with which the extracted knowledge terms occur within electronicdocuments originated by a user, and prior history of use of theextracted knowledge terms within an organization may furthermore beutilized to attach a metric, in the form of a confidence level value, tothe relevant knowledge terms for the purpose of grouping, ranking, andprioritizing such knowledge terms. Knowledge terms may furthermore bestored in either a private or public portion of the user knowledgeprofile, depending upon the confidence level values thereof.

It will be appreciated that the large volume of e-mail messagestraversing an e-mail system over a period of time will contain a largenumber of terms that may be irrelevant to the identification of theknowledge base of a user. With a view to determining which terms aretruly indicative of a knowledge base, a number of rules (or algorithms)may be exercised with respect to extracted terms to identify terms thatare candidates for inclusion within a public portion of the userknowledge profile. Further rules (or algorithms) may be applied to anassembled knowledge profile for the purpose of continually organizingand refining the profile.

Corporate e-mail systems have become increasingly pervasive, and havebecome an accepted medium for idea communication within corporations.Accordingly, the content of e-mail messages flowing within a largeorganization amounts to a vast information resources that, over thecourse of time, may directly or indirectly identify knowledge bases heldby individuals within the organization.

The present specification also teaches addressing privacy concernsassociated with the examination of e-mail messages for the abovepurposes by providing users with the option selectively to submitoriginated e-mail messages for examination, or alternatively to bypassthe examination and extraction system of the present invention.

There is also taught a computer-implemented method and apparatus foraddressing an electronic document, such as an e-mail message, fortransmission over a computer network. The e-mail message may be examinedto identify terms therein. The identified terms are then compared to anumber of user knowledge profiles with a view to detecting apredetermined degree of correspondence between the identified terms andany one or more of the user knowledge profiles. In the event that apredetermined degree of correspondence is detected, the sender of theelectronic document is prompted to the either accept or decline theproposed recipient as an actual recipient of the electronic document,after first being offered an opportunity to inspect the specific basisof the correspondence between the identified terms and the proposedrecipients. The e-mail message may also be parsed to extract recipientsentered manually by the user. The degree of correspondence between theknowledge profiles of the manually entered recipients and the identifiedterms of the message is then optionally used as the basis ofrecommendations to the user that certain manually entered recipients bedropped from the ultimate list of recipients.

This aspect of the present teachings is advantageous in that a sender ofan e-mail message is presented with a list of proposed recipients,identified according to their knowledge profiles and the content of thee-mail message, who may be interested in receiving the e-mail message.Accordingly, the problems of over-distribution and under-distribution ofe-mail messages that may be encountered within an organization may bereduced. Specifically, in the over-distribution situation, many usersare frequently copied on e-mail messages, resulting in lost productivityas the users struggle to cope with increasing volumes of daily e-mail.Further, when the time available to read e-mail messages becomesrestricted, users typically begin to defer reading of e-mail messages,and communication efficiency within the organization may be adverselyaffected. In the under-distribution situation, it may occur that theproper recipients of the message are not included in the distributionlist, and accordingly fall “out of the loop”.

There is also taught a method of facilitating a user profile query orlook-up wherein, in response to a match between a query and a userprofile, the owner of the user profile may be prompted for authorizationto publish all (or a portion) of the user profile to the originator ofthe query or to others generally. This is advantageous in that itaddresses the above mentioned privacy concerns by treating the knowledgeprofile as a confidential resource under the control of the user. Theuser is thus also able to control the timing, circumstances and extentto which it is made accessible to others. A further advantage is thatthe user is prompted for input specifically to satisfy specific, pendingrequests of others. This relieves the user of the need to remember tomodify his or her profile on a regular basis and the need to makedecisions concerning the composition of the profile prospectively, priorto any actual use of the profile by others. In this manner the usersaves time and effort, since the determination that manual interactionwith the profile is necessary is a function of the present system, not aresponsibility of the user.

There is also taught a method of assigning a confidence level value to aterm within an electronic document. This confidence level value is basedon a first quantitative indicator, derived from the number ofoccurrences of the term within the electronic document, and a secondcharacteristic indicator, derived utilizing the characteristic of theterm.

For the purposes of the present application, the word “term” shall betaken to include any acronym, word, collection of words, phrase,sentence, or paragraph. The term “confidence level” shall be taken tomean any indication, numeric or otherwise, of a level within apredetermined range.

SYSTEM ARCHITECTURE

FIG. 1 is a block diagram illustrating a knowledge management system 10,according to an exemplary embodiment of the present invention. Thesystem 10 may conveniently be viewed as comprising a client system 12and a server system 14. The client system 12 may comprise one or moreclients, such as browser clients 16 and e-mail clients 18, that areresident on terminals or computers coupled to a computer network. In oneexemplary embodiment, each of the browser clients 16 may comprise theInternet Explorer client developed by Microsoft Corp. of Redmond, Wash.,or the Netscape Navigator client developed by Netscape Communications ofMenlo Park, Calif. Each of the e-mail clients 18 may further comprisethe Outlook Express, Outlook 97, Outlook 98 or Netscape Communicatore-mail programs. As will be described in further detail below, thebrowser and e-mail clients 16 are complemented by extensions 19, thatenable the e-mail clients 18 to send an electronic message (e.g., eitheran e-mail or HTML document) to a knowledge server 22 implemented on theserver side 14 of the system 10. As shown in FIG. 1, the extensions 19may be integral with an e-mail client 18, or external to the client 18and in communication therewith. The clients 16 and 18 may default tosending every communication to a relevant component of the knowledgeserver 22, while allowing a user specifically to designate acommunication not suitable for transmission to the knowledge server 22.The user designation may be facilitated through controls that areinstalled as software modules which interact with or modify an e-mailclient 18, and which cause messages to be copied to a special e-mailaddress (e.g., a Knowledge Server (KS) mailbox 25 maintained by a e-mailserver 23) associated with a knowledge server component. In the casewhere a client extension 19 for performing this automatic transmissionis not available, the user can manually add the e-mail address of the KSmailbox 25 to the list of recipients for the message. Further details inthis regard are provided below. Files embedded within an e-mail message,such as attachments, may also be selectively included or excluded fromthe capture process and may also be selectively included or excludedfrom retention in a knowledge repository.

The browser clients 16 are used as an additional means to submitdocuments to the knowledge server 22 at the discretion of a user. Thebrowser client 16 is used to access an interface application 34,maintained on a web server 20, which transmits documents to theknowledge server 22.

In alternate embodiments, a client may also propagate a list ofbookmarks, folders or directories to the knowledge server 22 for thepurpose of user knowledge profile construction.

SERVER SIDE ARCHITECTURE

The server side 14 of the system 10 includes the web server 20, thee-mail server 23 and the knowledge server 22. The web server 20 may beany commercially available web server program such as InternetInformation Server (IIS) from Microsoft Corporation, the NetscapeEnterprise Server, or the Apache Server for UNIX. The web server 20includes the interface application 34 for interfacing with the knowledgeserver 22. The web server 20 may run on a single machine that also hoststhe knowledge server 22, or may alternatively run along with theinterface application 34 on a dedicated web server computer. The webserver 20 may also be a group of web server programs running on a groupof computers to thus enhance the scalability of the system 10. As theweb server 20 facilitates access to a local view of a knowledgerepository 50, maintained by the knowledge access server 26, by thebrowser clients 16, the web server interface application 34 implementsknowledge application interfaces, knowledge management interfaces, userprofile creation and maintenance interfaces, and a server managementinterface. The web server 20 also facilitates knowledge profile queries,e-mail addressing to an e-mail client 18, and any other access to theknowledge server 22 using the standard HTTP (web) protocol.

The knowledge server 22 includes a knowledge site management server(KSMS) 27 and the knowledge access server (KAS) 26. The knowledge serveraccess 26 includes an interface that provides a local view of aknowledge repository 50, which is physically stored in the user database56A and a repository database 56B. The knowledge site management server27 is shown to have access to the local view of the knowledge repository50 maintained by the knowledge access server 26. The illustratedcomponents of the knowledge server 22 are collectively responsible forthe capture (termed “knowledge discovery”) of terms indicative of a userknowledge base and for the distribution of user knowledge profileinformation. Knowledge discovery may be done by the examination andprocessing of electronic documents, such as e-mail messages, which maybe propagated to the e-mail server 23 from an e-mail client 18 via theSimple Mail Transfer Protocol (SMTP), as shown at 32. Alternatively,knowledge discovery may be implemented by the examination of submissionsfrom a browser client 16 via the web server 20.

The knowledge server 22 includes the knowledge access server 26 and theknowledge site management server 27 as two separate and distinct serversystems in view of the divergent functions provided by the servers 26and 27. Specifically, the knowledge site management server 27 functionsprimarily to manage non-interactive processing (e.g., the extraction ofknowledge from inbound e-mail messages), to manage the user informationdatabase 56A, and to implement various centralized system managementprocesses. The knowledge site management server 27 does not communicateinteractively with clients 18, or with clients 16 except foradministrative functions. The knowledge access server 26, on the otherhand, functions primarily to respond to queries and updates from userssubmitted via clients, typically browser clients 16. Multiple instancesof a knowledge access server 26 may be required to support a largecorporate environment and to provide appropriate scalability; howeveronly one knowledge site management server 27, one user database 56A, andone repository database 56B typically exist in a working system. Insmall scale environments, the web server 20, knowledge access server 26,and knowledge site management server 27, and even the e-mail server 23,may all optionally be deployed on the same physical computer.

FIG. 2 is a block diagram illustrating an exemplary embodiment,according to the present invention, of the knowledge site managementserver 27. The server 27 is shown to include a socket front-end 40 tofacilitate communication with the web server 20 for administrativerequests, a request handler 44, a knowledge gathering system 28, aknowledge converter 24, and a variety of specialized controller modules45A-45C. The request handler 44, upon receiving a request from the webserver 20 via the interface application 34 and socket front-end 40,starts a session to process the request such as, for example, a requestby an authorized systems administrator to configure the behavior of theknowledge gathering system 28.

The knowledge gathering system 28 is shown in FIG. 2 to include anextraction controller 47, a mail system interface 42, and a termextractor 46 including confidence logic 45. The extraction controller 47commands the mail system interface 42 to retrieve messages submitted bythe e-mail client extensions 19 to the KS mailbox 25 on the e-mailserver 23 for the purpose of extraction and processing. The extractioncontroller 47 can request this continuously or periodically on ascheduled basis, so that messages can be processed at a convenient timewhen computing resources are lightly loaded, for example, overnight. Themail system interface 42 retrieves e-mail messages from the e-mailserver 23 using the Simple Mail Transfer Protocol (SMTP), Post OfficeProtocol 3 (POP3), or Internet Message Access Protocol 4 (IMAP4)protocols. The mail system interface 42 propagates electronic documentsdirectly to a term extractor 46, including confidence logic 45, thatoperates to convert electronic documents into per-user knowledgeprofiles that are stored in a knowledge repository 50. The termextractor 46 may include any commercially available term extractionengine (such as “NPTOOL” from LingSoft Inc. of Helsinki, Finland, or“Themes” from Software Scientific) that analyzes the electronicdocument, recognizes noun phrases in the document, and converts suchphrases to a canonical form for subsequent use by the confidence logic45 as candidate terms in a knowledge profile.

The term extractor 46 performs a variety of the steps when parsing anddecoding an electronic document, such as interpreting any specialattributes or settings encoded into the header of the message of thee-mail client 18, resolving the e-mail addresses of recipients againsteither the built-in user database or an external user database,preprocessing the electronic document, extracting noun-phrases from thetext as candidates for knowledge terms, processing these knowledgeterms, and storing summary information about the document and extractionprocess in the databases 56A and 56B. The term extractor 46 furtherdetects and strips out non-original texts, attachments and in some casesthe entire electronic document based on the document not meetingpredetermined minimum criteria. Further details regarding the exactprocedures implemented by the term extractor 46 will be provided below.Once the term extractor 46 has extracted the knowledge terms, theknowledge repository 50 is updated. Specifically, new terms are added,and repetitions of known terms are used to update the knowledgerepository 50.

The knowledge repository 50 is defined by a hierarchical structure ofclasses. The objects of these classes represent the knowledgeinformation that includes, inter alia, user profiles (includingknowledge profiles) and organizational structure, and are stored in twodatabases: the user database 56A and the repository database 56B. Therepository database 56B contains profile and repository information andcan use one of a number of commercial relational database managementsystems that support the Open DataBase Connectivity (ODBC) interfacestandard. A database interface 54 provides a logicaldatabase-independent class API to access the physical databases and toshield the complete server codes from accessing database native API sothat the server process can use any relational database managementsystem (RDMS). Because the repository database 56A is open to inspectionby systems administrators, and may be hosted on an existing corporatesystem, special measures may be taken to enhance the privacy ofinformation in the repository database 56B; for example, the repositorydatabase 56B contains no actual user names or e-mail addresses, butinstead may use encrypted codes to represent users in a manner that ismeaningful only in combination with the user database. The user database56A is a small commercial RDBMS embedded into the knowledge repository50 in such a way that it cannot be accessed except through theinterfaces offered by the system 10. The user database 56A containsencrypted identifying codes that allow the names of actual users to beassociated with e-mail addresses, login IDs, passwords, and profile andrepository information in the repository database.

A lexicon controller 45C is responsible for building tables ofassociated terms. Terms are considered “associated” with each other tothe extent that they tend to co-occur in close proximity within thedocuments of multiple users. The lexicon controller 45C manages thebackground process of data mining that is used to discover associationsbetween terms and record those in special association tables within therepository database 56B.

A profile controller 45B is a module that may optionally be includedwithin the knowledge site management server 27, and manages a queue ofpending, compute-intensive operations associated with updating profiles.Since the algorithm for the confidence level value calculation of a term(embodied in the confidence logic 45) depends on the total number ofdocuments profiled, the confidence level value for each and every termin a user's profile is technically obsolete when any document isprofiled. The profile controller 45B manages the “recalculation” ofprofiles. The actual operation is performed within the knowledge accessserver 26, which has a knowledge repository 50 interface.

A case controller 45A keeps track of open cases and initiatesnotifications to users concerning their status. A “case” is a pendingrequest from one user to another, as will be detailed below. Forexample, if a user requests an expert in a certain field via a clientbrowser client 16, the knowledge access server 26 matches the termagainst both the public and private portions of all user profiles. If ahigh confidence, but private, match is found, the system cannot revealthe identity of the matched person to the inquirer and must thereforeopen a “case”. The case places a notification in the profile “home” pageof the target user and/or transmits an e-mail message with a link backto that page. The target user may then (via a browser):

1. See the identity of the inquirer and the basis of the match.

2. See comments added by the inquirer.

3. Deny the request, at which point the case is closed.

4. Put a block on any further matches from that person or based on thatterm.

5. Go into the profile and edit the term responsible for the match.

6. Indicate that the case is accepted and provide authorization toreveal the identity of the target to the inquirer.

From the perspective of the inquirer, private matches are initiallyreturned with a match strength only and do not reveal the name of theperson or document matched. The user can then initiate cases for any orall of these private matches, based on how urgently the information isneeded, how good the matches were, and whether the public matches aresufficient. Each case gets an expiration date set by the inquirer andnotification options regarding how the inquirer wants to be told aboutthe disposition of the case. Open cases are summarized in the Web areafor the inquirer, along with the date and query that generated thereturn values. If the target denies a case, that status is communicatedto the user. The user has no option to send e-mail or otherwise furtheridentify that person. If the target accepts the case, the identity ofthe target is communicated to the user by updating the case record andthe case is closed. Case history retention options are a siteadministration option.

FIG. 3 is a block diagram illustrating the components that constitutethe knowledge access server 26. The knowledge access server 26 is shownto include a socket front-end 40 to facilitate communication with theweb server interface application 34. The knowledge access server 26further includes a request handler 44, a term extractor 46, a knowledgerepository 50 and a database interface 54 that function in a mannersimilar to that described above with reference to the knowledgegathering system 28. The term extractor 46 includes comparison logic 51,the functioning of which will be described below. The knowledge accessserver 26 functions primarily as an interface between knowledge usersand the knowledge repository 50. It provides services to the web serverinterface application 34, which implements a number of user interfacesas described above for interacting with the knowledge repository 50.

FIG. 4 is a block diagram illustrating the components that constitutethe knowledge converter 24. The knowledge converter 24 is shown toinclude a term extractor 46 that is fed from an array of formatconverters 60. The knowledge converter 24 is able to access theknowledge repository 50, and to import data from other knowledgesystems, or export knowledge to other knowledge systems, via each of theformat converters 60.

Returning to FIG. 1, the knowledge access server 26 implements theinterface to the knowledge repository 50 and the knowledge sitemanagement server 27 is shown to access the knowledge repository 50 viathe knowledge access server 26. FIGS. 3 and 4 illustrate data for theknowledge repository 50 as residing in databases 56A and 56B. Thedatabases 56A and 56B are built on a general database interface 54 andprovide persistent storage for the core system classes referred toabove. In one exemplary embodiment of the present invention, the userdatabase and the repository databases are implemented utilizing theMicrosoft SQL server, developed by Microsoft Corp. of Redmond Wash., toprovide default storage management services for the system. However,programming may be done at a more general level to allow forsubstitution of other production class relational database managementsystems, such as those developed by Sybase, Oracle or Informix.

CLIENT SIDE ARCHITECTURE

FIG. 5 is a diagrammatic representation of a client, according to anexemplary embodiment of the present invention, in the form of an e-mailclient 18. It will be appreciated that the e-mail client 18 may be anycommercially available e-mail client, such as a Microsoft Exchange,Outlook Express, Outlook 97/98 or Lotus Notes client. The e-mail client18 includes modifications or additions, in the form of the extensions19, to the standard e-mail client to provide additional functionality.Specifically, according to an exemplary embodiment of the presentinvention, three subsystems are included within the e-mail clientextensions 19, namely a user interface 80, a profiling system 82, and anaddressing system 84.

The profiling system 82 implements properties on an originated message,as well as menu and property sheet extensions at global and messagelevels for users to set and manipulate these new properties. Morespecifically, profiling system 82 provides a user with a number ofadditional options that determine how a message 85 propagated from thee-mail client 18 to the knowledge repository 50 will be processed andhandled for the purposes of knowledge management. A number of theprovided options are global, while others apply on a per-message basis.For example, according to one exemplary embodiment, the followingpermessage options (or flags) may be set by a user to define theproperties of an e-mail message:

1. An “Ignore” flag 86 indicating the e-mail message should not beprocessed for these purposes of constructing or maintaining a userknowledge profile, and should not be stored.

2. A “Repository” parameter 88 indicating that the message may beprocessed for the purposes of constructing a knowledge profile and thenstored in the repository 50 for subsequent access as a document byothers. The “Repository” parameter 88 also indicates whether thedocument (as opposed to terms therein) is to be stored in a private orpublic portion of the repository 50.

A number of global message options may also be made available to a userfor selection. For example, an e-mail address (i.e., the KS mailbox 25or the e-mail server 23) for the knowledge server 22 may be enabled, sothat the e-mail message is propagated to the server 22.

Actual implementation and presentation of the above per-message andglobal options to the user may be done by the addition of a companionapplication or set of software modules which interact with API'sprovided by e-mail clients, or modules which modify the e-mail clientitself, which are available during message composition. If the useractivates the Ignore flag 86, the profiling system 82 will not make anymodifications to the message and no copy of the message will be sent tothe knowledge gathering system 28 via the KS mailbox 25. Otherwise,per-message options, once obtained from the user, are encoded.Subsequently, when the user chooses to send the message 85 using theappropriate control on the particular e-mail client 18, the e-mailaddress of the knowledge gathering server is appended to the blind copylist for the message. The profiling system 82 encrypts and encodes thefollowing information into the message header, for transmission to anddecoding by the knowledge gathering system 28, in accordance withInternet specification RFC 1522:

1. The list of e-mail addresses in the “to:” and “cc:” lists;

2. Per-message options as appropriate; and

3. For those recipients suggested by the addressing system 84 (seebelow), a short list of topic identifiers including the primary topicsfound within the message-and the primary topics found within the userprofile that formed a basis of a match.

4. Security information to validate the message as authentic. When themessage 85 is sent over the normal e-mail transport, the followingevents occur:

1. Recipients on the “to:” and “cc:” lists will receive a normal messagewith an extra header containing the encoded and encrypted options. Thisheader is normally not displayed by systems that read e-mail and can beignored by recipients;

2. The recipients will not be aware that the knowledge gathering systemhas received a blind copy of the message; and

3. If the sender chooses to archive a copy of the message 85, the e-mailaddress of the knowledge gathering system 28 will be retained in the“bcc” field as a reminder that the message was sent to the knowledgegathering server.

Further details concerning the addressing system 86 will be discussedbelow.

THE REPOSITORY

FIG. 6 is a block diagram illustrating the structure of the repository50, according to one exemplary embodiment of the present invention, asconstructed from data contained in the repository database 56B, and theuser database 56A. The repository 50 is shown to include a number oftables, as constructed by a relational database management system(RDBMS). Specifically, the repository 50 includes a user table 90, aterm table 100, a document table 106, a user-term table 112, aterm-document table 120 and a user-document table 130. The user table 90stores information regarding users for whom knowledge profiles may beconstructed, and includes an identifier column 92, including unique keysfor each entry or record within the table 90. A name column 94 includesrespective names for users for whom knowledge profiles are maintainedwithin the repository 50. A department column 96 contains a descriptionof departments within an organization to which each of the users may beassigned, and an e-mail column 98 stores respective e-mail addresses forthe users. It will be appreciated that the illustrated columns aremerely exemplary, and a number of other columns, storing furtherinformation regarding users, may be included within the user table 90.

The term table 100 maintains a respective record for each term that isidentified by the term extractor 46 within an electronic document, andthat is included within the repository 50. The term table 100 is shownto include an identifier column 102, that stores a unique key for eachterm record, and a term column 104 within which the actual extracted andidentified terms are stored. Again, a number of further columns mayoptionally be included within the term table 100. The document table 106maintains a respective record for each document that is processed by theterm extractor 46 for the purposes of extracting terms therefrom. Thedocument table 106 is shown to include an identifier column 108, thatstores a unique key for each document record, and a document name column110, that stores an appropriate name for each document analyzed by theterm extractor 46.

The user-term table 112 links terms to users, and includes at least twocolumns, namely a user identifier column 114, storing keys identifyingusers, and a term identifier column 116, storing keys identifying terms.The user-term table 112 provides a many-to-many mapping of users toterms. For example, multiple users may be associated with a single term,and a single user may similarly be associated with multiple terms. Thetable 112 further includes a confidence level column 118, which storesrespective confidence level values, calculated in the manner describedbelow, for each user-term pair. The confidence level value for eachuser-term pair provides an indication of how strongly the relevant termis coupled to the user, and how pertinent the term is in describing, forexample, the knowledge base of the relevant user.

The term-document table 120 links terms to documents, and provides arecord of which terms occurred within which document. Specifically, theterm-document table 120 includes a term identifier column 122, storingkeys for terms, and a document identifier column 124, storing keys fordocuments. The table 120 further includes an adjusted count column 126,which stores values indicative of the number of occurrences of a termwithin a document, adjusted in the manner described below. For example,the first record within the table 120 records that the term “network”occurred within the document “e-mail 1” 2.8 times, according to theadjusted count.

The user-document table 130 links documents to users, and includes atleast two columns, namely a user identifier column 132, storing keysidentifying users, and a document identifier column 134, storing keysidentifying various documents. For example, the first record within theexemplary user-document table 130 indicates that the user “Joe” isassociated with the document “e-mail 1”. This association may be basedupon the user being the author or recipient of the relevant document.

IDENTIFICATION OF KNOWLEDGE TERMS AND THE CALCULATION OF ASSOCIATEDCONFIDENCE LEVEL VALUES

FIG. 7 is a flow chart illustrating a method 140, according to anexemplary embodiment of the present invention, of constructing a userknowledge profile. FIG. 7 illustrates broad steps that are described infurther detail with reference to subsequent flow charts and drawings.The method 140 commences at step 142, and proceeds to decision box 144,wherein a determination is made as to whether an electronic document,for example in the form of an e-mail propagated from an e-mail client18, is indicated as being a private document. This determination may bemade at the e-mail client 18 itself, at the e-mail server 23, or evenwithin the knowledge site management server 27. This determination mayfurthermore be made by ascertaining whether the Ignore flag 86,incorporated within an e-mail message 85, is set to indicate the e-mailmessage 85 as private. As discussed above, the Ignore flag 86 may be setat a users discretion utilizing the profiling system 82, accessed viathe user interface 80 within the extensions 19 to the e-mail client 18.In the event that the electronic document is determined to be private,the method 140 terminates at step 146, and no further processing of theelectronic document occurs. Alternatively, the method 140 proceeds tostep 148, where confidence level values are assigned to various termswithin the electronic document. At step 150, a user knowledge profile isconstructed utilizing the terms within the electronic document to whichconfidence level values were assigned at step 148. The method 140 thenterminates at step 146.

FIG. 8 is a flow chart illustrating a high-level method 148, accordingto an exemplary embodiment of the present invention, by which terms maybe extracted from an electronic document, and by which confidence levelvalues may be assigned such terms. The method 148 comprises two primaryoperations, namely a term extraction operation indicated at 152, and aconfidence level value assigning operation, indicated at a 154. Themethod 148 implements one methodology by which the step 148 shown inFIG. 7 may be accomplished. The method 148 begins at step 160, and thenproceeds to step 162, where an electronic document, such as for examplean e-mail, a database query, a HTML document and or a database query, isreceived at the knowledge site management server 27 via the mail systeminterface 42. For the purposes of explanation, the present example willassume that an e-mail message, addressed to the KS mailbox 25, isreceived at the knowledge site management server 27 via the mail systeminterface 42, from the e-mail server 23. At step 164, terms andassociated information are extracted from the electronic document.Specifically, the e-mail message is propagated from the mail systeminterface 42 to the term extractor 46, which then extracts terms in theform of, for example, grammar terms, noun phrases, word collections orsingle words from the e-mail message. The term extractor 46 may furtherparse a header portion of the e-mail to extract information therefromthat is required for the maintenance of both the repository and userdatabases 56B and 56A. For example, the term extractor 46 will identifythe date of transmission of the e-mail, and all addressees. The termextractor 46 will additionally determine further information regardingthe electronic document and terms therein. For example, the termextractor 46 will determine the total number of words comprising theelectronic document, the density of recurring words within the document,the length of each term (i.e., the number of words that constitute theterm), the part of speech that each word within the documentconstitutes, and a word type (e.g., whether the word is a lexicon term).To this end, the term extractor 46 is shown in FIG. 2 to have access toa database 49 of lexicon terms, which may identify both universallexicon terms and environment lexicon terms specific to an environmentwithin which the knowledge site management server 27 is being employed.For example, within a manufacturing environment, the collection ofenvironment lexicon terms will clearly differ from the lexicon termswithin an accounting environment.

Following the actual term extraction, a first relevancy indicator in theform of an adjusted count value is calculated for each term within thecontext of the electronic document at step 168. At step 170, a secondrelevancy indicator in the form of a confidence level is calculated foreach term within the context of multiple electronic documents associatedwith a particular user. Further details regarding steps 168 and 170 areprovided below. The method 148 then terminates at step 172.

FIG. 9A is a flow chart illustrating a method 154, according to anexemplary embodiment of the present invention, of determining aconfidence level for a term extracted from an electronic document.Following the commencement step 180, a term and associated informationis received at the confidence logic 45, included within the termextractor 46. While the confidence logic 45 is shown to be embodied inthe term extractor 46 in FIG. 2, it will be appreciated that theconfidence logic 45 may exist independently and separately of the termextractor 46. In one embodiment, the associated information includes thefollowing parameters:

1. A count value indicating the number of occurrences of the term withina single electronic document under consideration;

2. A density value, expressed as a percentage, indicating the number ofoccurrences of the term relative to the total number of terms within theelectronic document;

3. A length of value indicating the total number of words includedwithin the relevant term;

4. A Part of Speech indication indicating the parts of speech that wordsincluded within the term comprise (e.g., nouns, verbs, adjectives, oradverbs); and

5. A Type indication indicating whether the term comprises a universallexicon term, an environment lexicon term, or is of unknown grammaticalstructure.

At step 184, a “binding strength”, indicative of how closely the term iscoupled to the electronic document under consideration, is determined.While this determination may be made in any number of ways, FIG. 10shows an exemplary term-document binding table 200, utilizing which aclass may be assigned to each of the extracted terms. Specifically, theterm-document binding table 200 is shown to include three columns,namely a “number of occurrences” column 202, a density column 204, andan assigned class column 206. A term having a density value of greaterthan four percent, for example, is identified as falling in the “A”class, a term having a density of between two and four percent isidentified as falling in the “B” class, a term having a density ofbetween one and two percent is identified as falling in the “C” class,while a term having a density of between 0.5 and one percent isidentified as falling in the “D” class. For the terms having a densityof above 0.5 percent, the density value is utilized to assign a class.For terms which have a density value less than 0.5 percent, the countvalue is utilized for this purpose. Specifically, a term having a countvalue of greater than 3 is assigned to the “E” class, and a term havinga count value of between 1 and 3 is assigned to the “F” class.Accordingly, the assigned class is indicative of the “binding strength”with which the term is associated with or coupled to the electronicdocument under consideration.

At step 186, a characteristic (or qualitative) indicator in the form ofa term weight value is determined, based on characteristics qualities ofthe term such as those represented by the Type and Part of Speechindications discussed above. While this determination may again be madein any number of ways, FIG. 11 shows an exemplary weight table 210,utilizing which a weight value may be assigned to each of the extractedterms. Specifically, the weight table 210 is shown to include fourcolumns, namely a weight column 212, a type column 214, a length column216 and a Part of Speech column 218. By identifying an appropriatecombination of type, length and Part of Speech indications, anappropriate term weight value is assigned to each term. In the typecolumn 214, a type “P” indication identifies an environment lexiconterm, a type “L” indication identifies a universal lexicon term, and atype “U” indication identifies a term of unknown grammatical structurefor a given length. The entries within the length column 216 indicatethe number of words included within the term. The entries within thePart of Speech column 218 indicate the parts of speech that the wordswithin a term comprise. The “A” indication identifies the adjectives,the “V” indication identifies a verb, the “N” indication identifies anoun, and the “X” indication identifies an unknown part of speech. Bymapping a specific term to an appropriate entry within the weight table210, an appropriate term weight value, as indicated in the weight column212, may be assigned to the term.

At step 188, a relevancy quantitative indicator in the form of anadjusted count value for each term, is calculated, this adjusted countvalue being derived from the binding strength and term weight valuescalculated at steps 184 and 186. While this determination may again bemade in any number of ways, FIG. 12 shows an exemplary occurrence factortable 220, utilizing which an adjusted count value for the relevant termmay be determined. The occurrence factor table 220 is shown to includevalues for various binding strength/term weight value combinations. Theadjusted count value is indicative of the importance or relevance ofterm within a single, given document, and does not consider theimportance or relevance of the term in view of any occurrences of theterm in other electronic documents that may be associated with aparticular user.

At step 190, a determination is made as to whether any adjusted countvalues exists for the relevant term as a result of the occurrence of theterm in previously received and analyzed documents. If so, the adjustedcount values for occurrences of the term in all such previous documentsare summed.

At step 192, an initial confidence level values for the term is thendetermined based on the summed adjusted counts and the term weight, asdetermined above with reference to the weight table 210 shown in FIG.11. To this end, FIG. 13 illustrates a confidence level table 230, whichincludes various initial confidence level values for various summedadjusted count/weight value combinations that may have been determinedfor a term. For example, a term having a summed adjusted count of 0.125,and a weight value of 300, may be allocated an initial confidence levelvalue of 11.5. Following the determination of an initial confidencelevel value, confidence level values for various terms may be groupedinto “classes”, which still retain cardinal meaning, but whichstandardize the confidence levels into a finite number of “confidencebands”. FIG. 14 illustrates a modified table 240, derived from theconfidence level table 230, wherein the initial confidence levelsassigned are either rounded up or rounded down to certain values. Bygrouping into classes by rounding, applications (like e-mailaddressing), can make use of the classes without specificknowledge/dependence on the numerical values. These can then be tunedwithout impact to the applications. The modified confidence level valuesincluded within the table 240 may have significance in a number ofapplications. For example, users may request that terms with aconfidence level of greater than 1000 automatically be published in a“public” portion of their user knowledge profile. Further, e-mailaddressees for a particular e-mail may be suggested based on a matchbetween a term in the e-mail and a term within the user knowledgeprofile having a confidence level value of greater than, merely forexample, 600.

The method 154 then terminates at step 194.

In a further embodiment of the present invention, the method 154,illustrated in FIG. 9A, may be supplemented by a number of additionalsteps 195, as illustrated in FIG. 9B, by which a “document weight” valueis assigned to a document based on addressee information associated withthe document. The document weight value may be utilized in any one ofthe steps 182-192 illustrated in FIG. 9A, for example, as a multiplyingfactor to calculate a confidence level value for a term. In oneexemplary embodiment, the binding strength value, as determined at step184, may be multiplied by the document weight value. In anotherexemplary embodiment, the term weight value, as determined at step 186,may be multiplied by the document weight value.

The document weight value may be calculated by the confidence logic 45within the term extractor 46. Referring to FIG. 9B, at step 196, theconfidence logic 45 identifies the actual addressee information. To thisend, the term extractor 46 may include a header parser (not shown) thatextracts and identifies the relevant addressee information. At step 197,the confidence logic 45 then accesses a directory structure that may bemaintained by an external communication program for the purposes ofdetermining the level of seniority within an organization of theaddressees associated with the document. In one exemplary embodiment ofthe invention, the directory structure may be a Lightweight DirectoryAccess Protocol (LDAP) directory maintained by a groupware server, suchas Microsoft Exchange or Lotus Notes. At step 198, a cumulativeseniority level for the various addressees is determined by summingseniority values for each of the addressees. At step 199, the summedseniority value is scaled to generate the document weight value. In thisembodiment, the cumulative or summed seniority level of the variousaddressees comprises an “average” seniority value that is used for thepurpose of calculating the document weight term. Alternatively, insteadof summing in the seniority values at step 198, a “peak” seniority value(i.e., a seniority value based on the seniority level of the most senioraddressee) may be identified and scaled at step 199 to generate thedocument weight value.

In alternative embodiments, the addressee information may be utilized ina different manner to generate a document weight value. Specifically, adocument weight value may be calculated based on the number ofaddressees, with a higher number of addressees resulting in a greaterdocument weight value. Similarly, a document weight value may becalculated based on the number of addressees who are included within aspecific organizational boundary (e.g., a specific department ordivision). For example, an e-mail message addressed primarily to anexecutive group may be assigned a greater document weight value than ane-mail message addressed primarily to a group of subordinates. Further,the document weight value may also be calculated using any combinationof the above discussed addressee information characteristics. Forexample, the document weight value could be calculated using bothaddressee seniority and addressee number information.

CONSTRUCTION OF A USER KNOWLEDGE PROFILE

FIG. 15A is a flow chart illustrating a method 250, according to oneexemplary embodiment of the present invention, of constructing a userprofile that includes first and second portions that may conveniently beidentified as “private” and “public” portions. Specifically,unrestricted access to the “public” portion of the user knowledgeprofile may be provided to other users, while restricted access to the“private” portion may be facilitated. For example, unrestricted accessmay encompass allowing a user to review details concerning a userknowledge profile, and the target user, responsive to a specific requestand without specific authorization from the target user. Restrictedaccess, on the other hand, may require specific authorization by thetarget user for the provision of information concerning the userknowledge profile, and the target user, in response to a specificrequest. The method 250 commences at step 252, and then proceeds to step254, where a determination is made regarding the confidence level valueassigned to a term, for example using the method 154 described abovewith reference to FIG. 9A. Having determined the confidence level value,the method 250 proceeds to step 256, where a threshold value isdetermined. The threshold value may either be a default value, or a userspecified value, and is utilized to categorize the relevant term. Forexample, users may set the threshold through the browser interface as afundamental configuration for their profile. If set low, the userprofile will be aggressively published to the public side. If set high,only terms with a high level of confidence will be published. Users canalso elect to bypass the threshold publishing concept altogether,manually reviewing each term that crosses the threshold (via thenotification manager) and then deciding whether to publish. At decisionbox 258, a determination is made as to whether the confidence levelvalue for the term is less than the threshold value. If so, this may beindicative of a degree of uncertainty regarding the term as being anaccurate descriptor of a user's knowledge. Accordingly, at step 260, therelevant term is then stored in the “private” portion of the userknowledge profile. Alternatively, should the confidence level value begreater than the threshold value, this may be indicative of a greaterdegree of certainty regarding the term as an accurate descriptor of auser's knowledge, and the relevant term is then stored in the “public”portion of the user's knowledge profile at step 262. The method 150 thenterminates at step 264.

FIG. 16A shows an exemplary user-term table 112, constructed accordingto the method 250 illustrated in FIG. 15A. Specifically, the table 112is shown to include a first user knowledge profile 270 and a second userknowledge profile 280. The first user knowledge profile 270 is shown toinclude a “public” portion 272, and a “private” portion 274, the termswithin the “private” portion 274 having an assigned confidence levelvalue (as indicated in the confidence level column 118) below athreshold value of 300. The second user knowledge profile 280 similarlyhas a “public” portion 282 and a “private” portion 284.

The exemplary user-term table 112 shown in FIG. 16A comprises anembodiment of the table 112 in which the public and private portions aredetermined dynamically with reference to a confidence level valueassigned to a particular user-term pairing. FIG. 16B illustrates analternative embodiment of the user-term table 112 that includes a“private flag” column 119, within which a user-term pairing may beidentified as being either public or private, and accordingly part ofeither the public or private portion of a specific user profile. Whilethe state of a private flag associated with a particular user-termpairing may be determined exclusively by the confidence level associatedwith the pairing, in an alternative embodiment of the invention, thestate of this flag may be set by other mechanisms. For example, asdescribed in further detail below with reference to FIG. 17E, a user maybe provided with the opportunity manually to modify the private orpublic designation of a term (i.e., move a term between the public andprivate portions of a user knowledge profile). A user may be providedwith an opportunity to modify the private or public designation of aterm in response to a number of events. Merely for example, a user maybe prompted to designate a term as public in response to a “hit” upon aterm in the private portion during a query process, such as during an“expert-lookup” query or during an “addressee-lookup” query. Whenstoring the term in the user knowledge profile at either steps 260 or262, the allocation of the term to the appropriate portion may be madeby setting a flag, associated with the term, in the “private flag”column 119 within the user-term table 112, as illustrated in FIG. 16B.For example, a logical “1” entry within the “private flag” column 119may identify the associated term as being in the “private” portion ofthe relevant user knowledge profile, while a logical “0” entry withinthe “private flag” column 119 may identify the associated term as beingin the “public” portion of the relevant user knowledge profile.

FIG. 15B illustrates an exemplary method 260/262, according to oneembodiment of the present invention, of storing a term in either apublic or private portion of a user knowledge profile. Specifically, arespective term is added to a notification list at step 1264, followingthe determination made at decision box 258, as illustrated in FIG. 15A.At decision box 1268, a determination is made as to whether apredetermined number of terms have been accumulated within thenotification list, or whether a predetermined time period has passed. Ifthese conditions are not met, the method waits for additional terms tobe added to the notification list, or for further time to pass, at step1266, before looping back to the step 1264. On the other hand, should acondition within the decision box 1268 have been met, the methodproceeds to step 1270, where the notification list, that includes apredetermined number of terms that are to be added to the user knowledgeprofile, is displayed to a user. The notification list may be providedto the user in the form of an e-mail message, or alternatively the usermay be directed to a web site (e.g., by a URL included within e-mailmessage) that displays the notification list. In yet a furtherembodiment, the notification list may be displayed on a web or intranetpage that is frequently accessed by the user, such as a home page. Atstep 1272, the user then selects terms that are to be included in thepublic portion of the user knowledge profile. For example, the user mayselect appropriate buttons displayed alongside the various terms withinthe notification list to identify terms for either the public or privateportions of the user knowledge profile. At step 1274, private flags,such as those contained within the “private flag” column 119 of theuser-term table 112 as shown in FIG. 16B, may be set to a logical zero“0” to indicate that the terms selected by the user are included withinthe public portion. Similarly, private flags may be set to a logical one“1” to indicate terms that were not selected by the user for inclusionwithin the public portion are by default included within the privateportion. It will of course be appreciated that the user may, at step1272, select terms to be included within the private portion, in whichcase un-selected terms will by default be included within the publicportion. The method then ends at step 1280.

The above described method is advantageous in that a user is notrequired to remember routinely to update his or her user profile, but isinstead periodically notified of terms that are candidates for inclusionwithin his or her user knowledge profile. Upon notification, the usermay then select terms for inclusion within the respective public andprivate portions of the user knowledge profile. As such, the method maybe viewed as a “push” model for profile maintenance.

METHOD OF ACCESSING A USER KNOWLEDGE PROFILE

While the above method 250 is described as being executed at the time ofconstruction of a user knowledge profile, it will readily be appreciatedthat the method may be dynamically implemented as required and inresponse to a specific query, with a view to determining whether atleast a portion of a user knowledge profile should be published, orremain private responsive to the relevant query. To this end, FIG. 17Ashows a flow chart illustrating a method 300, according to one exemplaryembodiment of the present invention, of facilitating access to a userknowledge profile. The method 300 commences at step 302, and thenproceeds to step 304, where a threshold value is determined. At step306, a document term within an electronic document generated by a user(hereinafter referred to as a “query” user) is identified. Step 306 isperformed by the term extractor 46 responsive, for example, to thereceipt of an e-mail from the mail system interface 42 within theknowledge gathering system 28. At step 308, comparison logic 51 withinthe term extractor 46 identifies a knowledge term within the repository50 corresponding to the document term identified at step 306. Thecomparison logic 51 also determines a confidence level value for theidentified knowledge term. At decision box 310, the comparison logic 51makes a determination as to whether the confidence level value for theknowledge term identified at step 308 is less than the threshold valueidentified at step 304. If not (that is the confidence level value isgreater than the threshold value) then a public profile process isexecuted at step 312. Alternatively, a private profile process isexecuted at step 314 if the confidence level value falls below thethreshold value. The method 300 then terminates at step 316.

FIG. 17B shows a flowchart illustrating an alternative method 301,according to an exemplary embodiment of the present invention, of =Pfacilitating access to a user knowledge profile. The method 301commences at step 302, and then proceeds to step 306, where a documentterm within an electronic document generated by a user (i.e., the“query” user) is identified. The term extractor 46 performs step 306responsive, for example, to the receipt of an e-mail message from themail system interface 42 within the knowledge gathering system 28. Atstep 308, the comparison logic 51 within the term extractor 46identifies a knowledge term within the knowledge repository 50corresponding to the document term identified at step 306. At decisionbox 311, the comparison logic 51 then makes a determination as towhether a “private” flag for the knowledge term is set to indicate therelevant knowledge term as being either in the public or the privateportion of a user knowledge profile. Specifically, the comparison logic51 may examine the content of an entry in the private flag column 112 ofa user-term table for a specific user-term pairing of which theknowledge term is a component. If the “private” flag for the knowledgeterm is set, thus indicating the knowledge term as being in the privateportion of a user knowledge profile, the private profile process isexecuted at step 314. Alternatively, the public profile process isexecuted at step 312. The method 301 then terminates at step 316.

FIG. 17C shows a flow chart detailing a method 312, according to anexemplary embodiment of the present invention, of performing the publicprofile process mentioned in FIGS. 17A and 17B. The method 312 commencesat step 320, and user information, the knowledge term corresponding tothe document term, and the confidence level value assigned to therelevant knowledge term are retrieved at steps 322, 324, and 326. Thisinformation is then displayed to the query user at step 328, whereafterthe method 312 terminates at step 330.

FIG. 17D shows a flow chart detailing a method 314, according to anexemplary embodiment of the present invention, of performing the privateprofile process mentioned in FIGS. 17A and 17B. The method 314 commencesat step 340, and proceeds to step 342, where a user (herein afterreferred to as the “target” user) who is the owner of the knowledgeprofile against which the hit occurred is notified of the query hit.This notification may occur in any one of a number of ways, such as forexample via an e-mail message. Such an e-mail message may furtherinclude a URL pointing to a network location at which furtherinformation regarding the query hit, as well as a number of target useroptions, may be presented. At step 346, the reasons for the query hitare displayed to the target user. Such reasons may include, for example,matching, or similar, document and knowledge terms utilizing which thehit was identified and the confidence level value associated with theknowledge term. These reasons may furthermore be presented within thee-mail propagated at step 342, or at the network location identified bythe URL embedded within the e-mail. At step 348, the target user thenexercises a number of target user options. For example, the target usermay elect to reject the hit, accept the hit, and/or modify his or heruser knowledge profile in light of the hit. Specifically, the targetuser may wish to “move” certain terms between the public and privateportions of the user knowledge profile. Further, the user may optionallydelete certain terms from the user knowledge profile in order to avoidany further occurrences of hits on such terms. These target user optionsmay furthermore be exercised via a HTML document at the network locationidentified by the URL. At decision box 350, a determination is made asto whether the user elected to modify the user knowledge profile. If so,a profile modification process, which is described below with referenceto FIG. 17E, is executed at step 352. Otherwise, a determination is madeat decision box 354 as to whether the target user rejected the hit. Ifso, the hit is de-registered at step 356. Alternatively, if the targetuser accepted the hit, the public profile process described above withreference to FIG. 17C is executed at step 358. The method 314 thenterminates at step 360.

FIG. 17E is a flowchart illustrating a method 352, according to anexemplary embodiment of the present invention, for implementing theprofile modification process illustrated at step 352 in FIG. 17D. Themethod 352 commences at step 362, and then proceeds to display step 364,where the target user is prompted to (1) move a term, on which a “hit”has occurred, between the private and public portions of his or her userknowledge profile, or to (2) delete the relevant term from his or heruser knowledge profile. Specifically, the target user may be presentedwith a user dialog, a HTML-enriched e-mail message, or a Web page,listing the various terms upon which hits occurred as a result of aninquiry, besides which appropriate buttons are displayed that allow theuser to designate the term either to the included in the public orprivate portion of his or her user knowledge profile, or that allow theuser to mark the relevant term for deletion from the user knowledgeprofile. At input step 366, the target user makes selections regardingthe terms in the matter described above. At decision box 368, adetermination is made as to whether the user selected terms for transferbetween the public and private portions of the user profile, or forinclusion within the user profile. If so, the method 352 proceeds tostep 370, wherein the appropriate terms are designated as being eitherpublic or private, in accordance with the user selection, by settingappropriate values in the “private flag” column 119 within the user-termtable, as illustrated in FIG. 16B. Thereafter, the method proceeds todecision box 372, wherein a determination is made as to whether the userhas elected to delete any of the terms presented at step 364. If so, therelevant terms are deleted from the user knowledge profile at step 374.The method is then terminates at step 378.

The methodologies described above with reference to FIGS. 15 through 17Eare advantageous in that, where the confidence level of a term fallsbelow a predetermined threshold, the owner of the user knowledge profilemay elect to be involved in the process of determining whether a queryhit is accurate or inaccurate. The owner of the user knowledge profileis also afforded the opportunity to update and modify his or herknowledge profile as and when needed. Further, the owner of the userknowledge profile is only engaged in the process for hits below apredetermined certainty level and on a public portion of the knowledgeprofile. Matches between document terms and knowledge terms in thepublic portion are automatically processed, without any manualinvolvement.

METHOD FOR ADDRESSING AN ELECTRONIC DOCUMENT FOR TRANSMISSION OVER ANETWORK

Returning now briefly to FIG. 5, the addressing system 84 within thee-mail client extensions 19 operates independently of the profilingsystem 82 to suggest potential recipients for an e-mail message based onthe content thereof. The user interface 80 within the e-mail clientextensions 19 may pop-up a window when the system determines suchsuggestion is possible, based on the length of a draft message beingsent, or may present a command button labeled “Suggest Recipients”. Thisbutton is user selectable to initiate a sequence of operations wherebythe author of the e-mail is presented with a list of potentialrecipients who may be interested in receiving the e-mail based onpredetermined criteria, such as a match between the content of thee-mail and a user profile, or a commonality with a confirmed addressee.

FIG. 18A is a flow chart illustrating a method 400, according to anexemplary embodiment of the present invention, of addressing anelectronic document, such as an e-mail, for transmission over a network,such as the Internet or an Intranet. The method 400 commences at step402, and then proceeds to step 401, where a determination is made as towhether the body of the draft message exceeds a predetermined length (ornumber of words). If so, content of the electronic document (e.g., ane-mail message body) is transmitted to the knowledge access server 26via the web server 20 at step 404. Specifically, a socket connection isopen between the e-mail client 18 and the web server 20, and the contentof the message body, which may still be in draft form, is transmittedusing the Hypertext Transfer Protocol (HTTP) via the web server 20 tothe knowledge access server 26. At step 406, the knowledge access server26 processes the message body, as will be described in further detailbelow. At step 408, the knowledge access server 26 transmits a potentialor proposed recipient list and associated information to the addressingsystem 84 of the e-mail client 18. Specifically, the informationtransmitted to the e-mail client 18 may include the following:

1. A list of user names, as listed within column 94 of the user table90, as well as corresponding e-mail addresses, as listed within thecolumn 98 of the user table 90;

2. A list of term identifiers, as listed in column 116 of the user-termtable 112, that were located within the “public” portion of a userknowledge profile that formed the basis for a match between documentterms within the message body and knowledge terms within the userknowledge profile; and

3. A “matching metric” for each user included in the list of user names(1). Each “matching metric” comprises the sum of the confidence levelvalues, each multiplied by the weighted occurrences of the term withinthe message body, for the terms identified by the list of termidentifiers (2) and associated with the relevant user. This “matchingmetric” is indicative of the strength of the recommendation by theknowledge access server 26 that the relevant user (i.e., potentialrecipient) be included within the list of confirmed addressees.

At step 410, the author of the electronic document is presented with alist of potential recipients by the e-mail client 18, and specificallyby the addressing system 84 via a user dialog 440 as shown in FIG. 18D.

FIG. 18D groups matching levels into matching classes each characterizedby a visual representation (icon).

The user dialog 440 shown in FIG. 18D presents the list of potentialrecipients in a “potential recipients” scrolling window 442, wherein thenames of potential recipients are grouped into levels or ranked classesaccording to the strength of the matching metric. An icon is alsoassociated with each user name, and provides an indication of thestrength of the recommendation of the relevant potential recipients.Merely for example, a fully shaded circle may indicate a highrecommendation, with various degrees of “blackening” or darkening of acircle indicating lesser degrees of recommendation. A “rejection” iconmay be associated with an actual recipient, and an example of such a“rejection” icon is indicated at 441. The “rejection” icon indicates anegative recommendation on an actual recipient supplied by the author ofthe message, and may be provided in response to a user manuallymodifying his or her profile to designate certain terms therein asgenerating such a “rejection” status for a recipient against which a hitoccurs.

The user dialog 440 also presents a list of actual (or confirmed)recipients in three windows, namely a “to:” window 442, a “cc:” window444 and a “bcc:” window 446. An inquiring user may move recipientsbetween the potential recipients list and the actual recipients listsutilizing the “Add” and “Remove” buttons indicated at 450. The userdialog 440 also includes an array of “select” buttons 452, utilizingwhich a user can determine the recommendation group to be displayedwithin the scrolling window 442. The user dialog 440 finally alsoincludes “Explained Match” and “More” buttons 454 and 456, the purposesof which is elaborated upon below. As shown in FIG. 18D, the author usermay select an “Explain” function for any of the proposed recipientsutilizing the “Explain Match” button 454. If it is determined atdecision box 412 that this “Explain” function has been selected, themethod 400 branches to step 414, as illustrated in FIG. 18B.Specifically, at step 414, the addressing system 84 propagates a further“Explain” query to the knowledge access server 26 utilizing HTTP, andopens a browser window within which to display the results of the query.At step 416, the knowledge access server 26 retrieves the terms (i.e.,the knowledge terms) that constituted the basis for the match, as wellas associated confidence level values. This information is retrievedfrom the public portion of the relevant user knowledge profile in theknowledge repository 50. At step 418, the information retrieved at step416 is propagated to the client 18 from the knowledge access server 26via the web server 20. The information is then displayed within thebrowser window opened by the e-mail client 18 at step 414. Accordingly,the author user is thus able to ascertain the reason for the proposal ofa potential recipient by the addressing system 84, and to make a moreinformed decision as to whether the proposed recipient should beincluded within the actual recipients (confirmed addressee) list.

The user also has the option of initiating a “More” function byselecting the “More” button 456 on the user dialog 440, this functionserving to provide the user with additional proposed recipients.Accordingly, a determination is made at step 422 as to whether the“More” function has been selected by the author user. If so, the method400 branches to step 424 as shown in FIG. 18C, where the client 18propagates a “More” request to the knowledge access server 20 in thesame manner as the “Explain” query was propagated to the knowledgeaccess server at step 414. At step 46, the knowledge access server 26identifies further potential recipients, for example, by using athreshold value for the “matching metric” that is lower than a thresholdvalue utilized as a cutoff during the initial information retrievaloperation performed at steps 406 and 408. At step 428, the knowledgeaccess server 26 then transmits the list of further potentialrecipients, and associated information, to the e-mail client 18. At step430, the list of additional potential recipients is presented to theauthor user for selection in descending order according to the “matchingmetric” associated with each of the potential recipients.

At step 432, the user then adds at his or her option, or deletesselected potential or “rejected” recipients to the list of actualrecipients identified in “to:”, “cc:” or “bcc:” lists of the e-mail,thus altering the status of the potential recipients to actualrecipients. At step 434, the e-mail message is then transmitted to theconfirmed addressees.

If the user profile includes a “rejection” status on a term (something auser can do through manual modification of the profile), then a specialsymbol, such as that indicated 441 in FIG. 18D, may be returnedindicating a negative recommendation on a recipient supplied by theauthor of the message.

The exemplary method 400 discussed above is advantageous in that theknowledge access server 26 automatically provides the author user with alist of potential addressees, based on a matching between document termsidentified within the message body of an e-mail and knowledge termsincluded within user profiles.

CASE CONTROL

FIG. 19 is a flow chart illustrating a method 500, according to oneexemplary embodiment of the present invention, of managing userauthorization to publish, or permit access to, a user knowledge profile.The method 500 is executed by the case controller 45A that tracks open“cases” and initiates notification to users concerning the status ofsuch cases. For the purposes of the present specification, the term“case” may be taken to refer to a user authorization process forpublication of, or access to, a user knowledge profile. The method 500commences at step 502, and then proceeds to step 504, where a match isdetected with a private portion of a user knowledge profile. At step504, the case controller 45A then opens a case, and notifies the targetuser at step 506 concerning the “hits” or matches between a document (orquery) term and a knowledge term in a knowledge user profile. Thisnotification may be by way of an e-mail message, or by way ofpublication of information on a Web page accessed by the user. At step508, the case controller 45A determines whether an expiration date, bywhich the target user is required to respond to the hit, has beenreached or in fact passed. If the expiration date has passed, the casecontroller 45A closes the case and the method 500 terminates.Alternatively, a determination is made at decision box 510 as to whetherthe target user has responded to the notification by authorizingpublication of, or access to, his or her user knowledge profile based onthe hit on the private portion thereof. If the target user has notauthorized such action (i.e., declined authorization), an inquiring user(e.g., the author user of an e-mail or a user performing a manualdatabase search to locate an expert) is notified of the decline at step512. Alternatively, should the target user have authorized publicationor access, the inquiring user is similarly notified of the authorizationat step 514. The notification of the inquiring user at steps 512 or 514may be performed by transmitting an e-mail to the inquiring user, or byproviding a suitable indication on a web page (e.g., a home page orsearch/query web page) accessed by the inquiring user. At step 516, theappropriate portions of the user profile pertaining to the target userare published to the inquiring user, or the inquiring user is otherwisepermitted access to the user profile. At step 518, the case controller45A then closes the case, whereafter the method terminates.

SUPPLEMENTAL METHOD OF IDENTIFYING CONFIDENCE VALUE

FIGS. 7-9 describe an exemplary method 140 of identifying knowledgeterms and calculating associated confidence level values. A supplementalmethod 550, according to an exemplary embodiment of the presentinvention, of assigning a confidence value to a term will now bedescribed with reference to FIGS. 20-22. The supplemental method 550seeks to compensate for a low confidence level value which may beassociated with the term as a result of the term not appearing in anyrecent documents associated with a user. It will be appreciated that bycalculating a confidence level value utilizing the method illustrated inFIG. 9, aged terms (i.e., terms which have not appeared in recentdocuments) may be attributed a low confidence level value even thoughthey may be highly descriptive of a specialization or knowledge of auser. The situation may occur where a user is particularly active withrespect to a particular topic for a short period of time, and thenre-focuses attention on another topic. Over time, the methodologyillustrated in FIG. 9 may too rapidly lower the confidence level valuesassociated with terms indicating user knowledge.

Referring to FIG. 20, there is illustrated the exemplary method 550 ofassigning a confidence value to a term. The method 550 commences at step552, whereafter an initial confidence memory value (as distinct from aconfidence level value) is assigned a zero (0) value. At step 556, aconfidence level value for a term is calculated utilizing, for example,the method 154 illustrates in FIG. 9. However, this confidence levelvalue is only calculated for occurrences of the relevant term within aparticular time or document window. For example, in summing the adjustedcount values at step 190 within the method 154, the adjusted countvalues for only documents received within a predetermined time (e.g.,the past 30 days), or only for a predetermined number of documents(e.g., the last 30 documents) are utilized to calculate the summedadjusted count value. It will be appreciated that by discardingdocuments, which occurred before the time or document window, the effecton the confidence level values for aged terms by the absence of suchaged terms within recent documents may be reduced.

At decision box 558, a determination is then made as to whether a newlycalculated confidence level value for a term is greater than apreviously recorded confidence memory value, or alternatively greaterthan a predetermined site-wide or system-wide threshold value. If theconfidence level value is determined to be greater than the confidencememory value (or the threshold value), the confidence memory value isthen made equal to the confidence level value by overwriting theprevious confidence memory value with the newly calculated confidencelevel value. In this way, it is ensured that the confidence level valuedoes not exceed the confidence memory value.

FIG. 22 is an exemplary user-term table 112, according to one embodimentof the present invention, that is shown to include a confidence levelcolumn 118, a confidence memory value column 121, and a time stampcolumn 123. The table 122 records a confidence level value and aconfidence memory value for each user-term pairing within the table 112,and it is to this table that the confidence level values and theconfidence memory values are written by the method 550. The time stampcolumn 123 records a date and time stamp value indicative of the dateand time at which the corresponding confidence memory value was lastupdated. This value will accordingly be updated upon the overwriting ofthe confidence memory value at step 560.

Should the confidence level value not exceed the confidence memory valueor the threshold value, as determined at decision box 558, the method550 then proceeds to decision box 562, where a further determination ismade as to whether another time or document window, associated with astep of decaying the confidence memory value, has expired. If not, theconfidence memory value is left unchanged at step 564. Alternatively, ifthe time or document window associated with the decay step has expired,the confidence memory value is decayed by a predetermined value orpercentage at step 566. For example, the confidence memory value may bedecayed by five (5) percent per month. The time stamp value may beutilized to determine the window associated with the decay step. Thetime stamp value associated with the decayed confidence memory value isalso updated at step 566. The method 550 then terminates at step 568.

FIG. 21 is a flowchart illustrating an exemplary method 570, accordingto one embodiment of the present invention, of determining oridentifying a confidence value (e.g., either a confidence level value ora confidence memory value) for a term. The method 570 may be executed inperformance of any of the steps described in the preceding flow chartsthat require the identification of a confidence level value for a termin response to a hit on the term by a document term (e.g., in anelectronic document or other query). The method 570 commences at step572, and proceeds to step 574, where a confidence level value for a termwithin a user profile is identified. For example, the confidence levelvalue may be identified within be user-term table 112 illustrated inFIG. 22. At step 576, a confidence memory value for the term may thenalso be identified, again by referencing the user-term table 112illustrated in FIG. 22. At decision box 578, a determination is thenmade as to whether the confidence level value is greater than theconfidence memory value. If the confidence level value is greater thanthe confidence memory value, the confidence level value is returned, atstep 580, as the confidence value. Alternatively, should the confidencememory value be greater than the confidence level value, the confidencememory value is returned, at step 582, as the confidence value. Themethod 570 then terminates at step 584.

Accordingly, by controlling the rate at which a confidence value for aterm is lowered or decayed, the present invention seeks to preventhaving a potentially relevant term ignored or overlooked.

COMPUTER SYSTEM

FIG. 23 is a diagrammatic representation of a machine in the form ofcomputer system 600 within which software, in the form of a series ofmachine-readable instructions, for performing any one of the methodsdiscussed above may be executed. The computer system 600 includes aprocessor 602, a main memory 603 and a static memory 604, whichcommunicate via a bus 606. The computer system 600 is further shown toinclude a video display unit 608 (e.g., a liquid crystal display (LCD)or a cathode ray tube (CRT)). The computer system 600 also includes analphanumeric input device 610 (e.g., a keyboard), a cursor controldevice 612 (e.g., a mouse), a disk drive unit 614, a signal generationdevice 616 (e.g., a speaker) and a network interface device 618. Thedisk drive unit 614 accommodates a machine-readable medium 615 on whichsoftware 620 embodying any one of the methods described above is stored.The software 620 is shown to also reside, completely or at leastpartially, within the main memory 603 and/or within the processor 602.The software 620 may furthermore be transmitted or received by thenetwork interface device 618. For the purposes of the presentspecification, the term “machine-readable medium” shall be taken toinclude any medium that is capable of storing or encoding a sequence ofinstructions for execution by a machine, such as the computer system600, and that causes the machine to performing the methods of thepresent invention. The term “machine-readable medium” shall be taken toinclude, but not be limited to, solid-state memories, optical andmagnetic disks, and carrier wave signals.

Thus, a method and apparatus for assigning a confidence level to a termwithin a user knowledge profile have been described. Although thepresent invention has been described with reference to specificexemplary embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

What is claimed is:
 1. A method of assigning a confidence level to aterm within an electronic document for a knowledge profile of a user,the knowledge profile having portions with different accessrestrictions, the method comprising: determining a first quantitativeindicator based on a number of occurrences of the term within theelectronic document, the electronic document originating from the user;determining a characteristic indicator based on a characteristic of theterm; and assigning a second indicator to the term, the second indicatorbeing derived from the first quantitative indicator and thecharacteristic indicator, and the confidence level of the term for theknowledge profile of the user being derived from the second indicator,wherein the term is automatically stored in one of the portionsaccording to the confidence level, with subsequent access to the termbeing restricted according to the portion in which the term is stored.2. The method of claim 1 wherein determining the first quantitativeindicator includes determining a number of occurrences of the termwithin the electronic document.
 3. The method of claim 1 whereindetermining the first quantitative indicator includes determining afrequency of occurrence of the term within the electronic document. 4.The method of claim 1 wherein determining the first quantitativeindicator includes determining a density of occurrence of the termwithin the electronic document.
 5. The method of claim 1 whereindetermining the characteristic indicator includes determining the numberof words comprising the term.
 6. The method of claim 1 whereindetermining the characteristic indicator includes determining parts ofspeech included within the term.
 7. The method of claim 1 whereindetermining the characteristic indicator includes determining whether aword within the term corresponds to a lexicon term.
 8. The method ofclaim 1 including summing second indicators, assigned to the term basedon occurrences of the term within a plurality of electronic documents,to generate a summed indicator.
 9. The method of claim 8 includingassigning a confidence level indicator to the term based on the summedindicator and the characteristic indicator.
 10. The method of claim 9including scaling the confidence level indicator.
 11. The method ofclaim 1 including determining a third indicator based on addresseeinformation associated with the electronic document.
 12. The method ofclaim 11 wherein determining the third indicator includes identificationof a level of seniority within an organization of an addressee of theelectronic document.
 13. The method of claim 11 wherein determining thethird indicator includes determining an average level of senioritywithin an organization of all addressees of the electronic document. 14.The method of claim 11 wherein determining the third indicator includesdetermining a level of seniority within an organization of a most senioraddressee of the electronic document.
 15. The method of claim 12including generating a document weight term utilizing the level ofseniority of the addressee of the electronic document.
 16. The method ofclaim 11 wherein determining the third indicator includes identificationof a number of addressees of the electronic document.
 17. The method ofclaim 16 including generating a document weight term utilizing thenumber of addressees of the electronic document.
 18. The method of claim11 wherein determining the third indicator includes identification of anorganizational group of at least one addressee of the electronicdocument.
 19. The method of claim 18 including generating a documentweight term utilizing the organizational group of the at least oneaddressee of the electronic document.
 20. Apparatus for assigning aconfidence level to a term within an electronic document for a knowledgeprofile of a user, the knowledge profile having portions with differentaccess restrictions, the apparatus comprising: a term extractor toextract the term from the electronic document, the electronic documentoriginating from the user; and confidence logic to determine a firstquantitative indicator based on a number of occurrences of the termwithin the electronic document; to determine a qualitative indicatorbased on a quality of the term; and to assign a second indicator,derived from the first quantitative indicator and the qualitativeindicator, to the term, wherein the confidence level of the term for theknowledge profile of the user is derived from the second indicator,wherein the term is automatically stored in one of the portionsaccording to the confidence level, with subsequent access to the termbeing restricted according to the portion in which the term is stored.21. The apparatus of claim 20 wherein the confidence logic determines anumber of occurrences of the term within the electronic document. 22.The apparatus of claim 20 wherein the confidence logic determines afrequency of occurrence of the term within the electronic document. 23.The apparatus of claim 20 wherein the confidence logic determines adensity of occurrence of the term within the electronic document. 24.The apparatus of claim 20 wherein the confidence logic determines thenumber of words comprising the term.
 25. The apparatus of claim 20wherein the confidence logic determines parts of speech included withinthe term.
 26. The apparatus of claim 20 wherein the confidence logicdetermines whether a word within the term corresponds to a lexicon term.27. The apparatus of claim 20 wherein the confidence logic sums secondindicators, assigned to the term based on occurrences of the term withina plurality of electronic documents, to generate a summed indicator. 28.The apparatus of claim 27 wherein the confidence logic assigns aconfidence level indicator to the term based on the summed quantitativeindicator and the qualitative indicator.
 29. The apparatus of claim 28wherein the confidence logic scales the confidence level indicator. 30.The apparatus of claim 20 wherein the confidence logic determines athird indicator utilizing addressee information associated with theelectronic document.
 31. The apparatus of claim 30 wherein theconfidence logic identifies a level of seniority within an organizationof an addressee of the electronic document.
 32. The apparatus of claim30 wherein the confidence logic determines an average level of senioritywithin an organization of all addressees of the electric document. 33.The apparatus of claim 30 wherein the confidence logic determines alevel of seniority within an organization of a most senior addressee ofthe electronic document.
 34. The apparatus of claim 30 wherein theconfidence logic determines a level of seniority within an organizationof a most senior addressee of the electronic document.
 35. The apparatusof claim 30 wherein the confidence logic identifies an organizationalgroup of at least one addressee of the electronic document. 36.Apparatus for assigning a confidence level to a term within anelectronic document for a knowledge profile of a user, the knowledgeprofile having portions with different access restrictions, theapparatus comprising: term extraction means for extracting a term fromthe electronic document, the electronic document originating from theuser; and confidence means for determining a first quantitativeindicator based on the number of occurrences of the term within theelectronic document; for determining a characteristic indicator based ona characteristic of the term; and for assigning a second indicator,derived from the first quantitative indicator and the characteristicindicator, to the term wherein the confidence level of the term for theknowledge profile of the user is derived from the second indicator,wherein the term is automatically stored in one of the portionsaccording to the confidence level, with subsequent access to the termbeing restricted according to the portion in which the term is stored.37. A machine-readable medium storing a sequence of instructions that,when executed by a machine, cause the machine to perform a methodcomprising: determining a first quantitative indicator based on a numberof occurrences of a term within an electronic document for a knowledgeprofile of a user, the electronic document originating from the user andthe knowledge profile having portions with different accessrestrictions; determining a qualitative indicator based on a quality ofthe term; and assigning a composite indicator to the term, the compositeindicator being derived from the first quantitative indicator and thequalitative indicator, and the confidence level of the term for theknowledge profile of the user being derived from the second indicator,wherein the term is automatically stored in one of the portionsaccording to the confidence level, with subsequent access to the termbeing restricted according to the portion in which the term is stored.